salary_calculatorConvert pay between hourly, daily, weekly, biweekly, monthly and annual. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Pay amount | |
| locale | No | Language for the source_url link (default en) | |
| period | Yes | Period the amount refers to | |
| hours_per_week | No | Working hours per week (default 40) | |
| weeks_per_year | No | Paid weeks per year (default 52) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the conversion action and a host URL (smart-tools.xyz), but fails to mention any side effects, output format, precision, or whether the operation is read-only. For a tool that likely performs calculations and returns results, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using one clear sentence for the core purpose. The added phrase "Runs on smart-tools.xyz" is short but not directly useful for tool invocation, so it slightly reduces efficiency. Overall, it is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple calculator with well-documented parameters, the description is mostly adequate, but it omits any details about the return value or output behavior. Since there is no output schema, the agent must infer what the tool returns. The platform reference does not compensate for this missing return-value context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all parameters, including enums for period and locale, and defaults for hours_per_week and weeks_per_year. The description adds no additional parameter-level detail, but the schema already handles semantics well, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb "Convert" with the resource "pay" and enumerates the exact periods (hourly, daily, weekly, biweekly, monthly, annual), making the tool's purpose unambiguous and distinguishing it from sibling converters/calculators. It is clear this is a salary/pay conversion tool, not a generic unit converter.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the tool is for converting pay amounts between different time periods, but it does not provide explicit when-to-use/when-not-to-use guidance or name any alternative tools. The context is sufficient for a basic calculator, but no exclusions or comparisons are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.